The Tool Desk
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What zero-code tracing adds—and what it does not
OpenTelemetry’s Python agent loads instrumentation for supported libraries at runtime, primarily by modifying instrumented library functions. This can produce spans for activity such as HTTP requests, database calls, and messaging without edits to your asset source. It does not automatically provide a complete trace of Dagster execution or every line of application logic. As OpenTelemetry puts it, “Your application’s code, however, is not typically instrumented.” OpenTelemetry zero-code instrumentation
That distinction matters if your debugging question is about which asset or op ran, how long a business-logic section took, or how work moved through Dagster. Library spans may not identify those application boundaries. Add code-based spans where explicit asset-, op-, or business-level visibility is necessary. Check the current Python instrumentation documentation and registry for the libraries and dependency versions in your deployment; support for one library does not imply coverage for all of its calls or versions.
Install and configure the Python agent
Run these steps in the Python environment used by each process you intend to trace. The OpenTelemetry Python guide documents both CLI and environment-variable configuration; the values below are configuration names, not universal backend endpoints.
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- Install the distribution and OTLP exporter. Add
opentelemetry-distroandopentelemetry-exporter-otlpto the target environment. - Install matching library instrumentation. In that same environment, run
opentelemetry-bootstrap -a install. Review the installed packages and confirm that the libraries important to your workload are supported. - Set the service identity and trace exporter. Configure a stable
OTEL_SERVICE_NAME, select OTLP for traces withOTEL_TRACES_EXPORTER, and setOTEL_EXPORTER_OTLP_TRACES_ENDPOINTto the trace endpoint required by your backend. Follow that backend’s endpoint and authentication requirements; example values in documentation are illustrative. - Launch the target entry point through the agent. Start the Dagster-related Python process with
opentelemetry-instrument, making sure it inherits the environment variables and can reach the configured endpoint. - Check the destination. Look for spans from the expected service and workload. If spans appear for one part of Dagster but not another, verify the missing process’s environment, startup command, agent installation, and network access.
Use the official Python zero-code setup for current commands and configuration details. The agent configures the interpreter it starts; merely installing packages in another Python environment does not instrument this process.
Put the agent where Dagster code runs
Dagster execution can cross process and infrastructure boundaries. An in-process executor, a multiprocess executor that starts steps in their own processes, and executors that send work to external systems do not have the same instrumentation scope. Dagster documents execution through environments including Kubernetes pods, ECS tasks, Docker containers, and Celery tasks in its run executor guide.
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Treat every separate runtime as an instrumentation target unless its launch mechanism explicitly injects the agent and configuration. A trace from the webserver or daemon does not establish that the run worker or step process is emitting spans. For each runtime, check which Python environment executes the code, whether child processes inherit the startup wrapper and OTEL settings, and whether the process has network access to the exporter endpoint.
In-process and multiprocess execution
With in-process execution, the relevant workload runs in the process that loads and executes it, so instrumenting that interpreter can capture supported library activity there. With multiprocess execution, steps run in their own processes; confirm that those child processes start with the agent and receive the required environment. Do not infer child-process coverage solely from seeing spans emitted by the parent.
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External tasks and containers
When an executor launches work outside the current process—such as a container, pod, or task—install the agent in that task’s Python environment and pass its service identity and OTLP configuration into the task. The parent process’s packages and environment are not a substitute for configuring an external runtime.
Instrument the right images in Docker Compose
Dagster’s documented Docker Compose arrangement separates services: the webserver and daemon run in containers, code locations have their own image, and runs in the example typically execute in their own containers using the code-location image. See Dagster’s Docker Compose deployment guide.
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Install the agent in every image whose Python activity should appear in the trace backend. Configure the relevant container or task with its service name and OTLP settings, and ensure its process starts through opentelemetry-instrument. Adding the package only to the webserver image will not instrument code running in a separate code-location or run image.
Account for the Dagster deployment mode
The right place to add the agent depends on whether you use Dagster OSS, Dagster+ Serverless, or Dagster+ Hybrid. These deployment modes can have different worker and image boundaries; identify where the Python code for your run executes before choosing where to install the agent. Dagster’s deployment overview describes the available deployment choices.
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The dagster.yaml reference documents instance-level deployment configuration and use of environment variables. That file alone does not install or load a Python agent inside each target interpreter. Configure the Python runtime that executes the work as well as any instance-level settings your deployment requires.
Diagnose missing spans by following the process boundary
- No spans from any process: Confirm the target environment has the distro and exporter, the process starts with
opentelemetry-instrument, the trace exporter is configured, and the endpoint is reachable. - Spans from Dagster services but not the run: Identify the run’s actual worker, container, pod, or external task. Install and configure the agent there rather than assuming the webserver or daemon’s setup applies.
- Some library calls appear, but not the asset or op boundary: Check whether the relevant libraries are supported, then add code-based spans for application-level boundaries that library instrumentation does not represent.
- Parent spans appear, but child-step activity is missing: Check the child process’s Python environment, startup command, inherited OTEL variables, and network access independently.
OpenTelemetry describes its framework as supported by more than 90 observability vendors on its documentation page, last modified August 29, 2025. That is an ecosystem figure, not a measure of Dagster compatibility or a guarantee that a particular backend accepts a given configuration. OpenTelemetry documentation
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